Association between neutrophil-albumin ratio and ultrasound-defined metabolic dysfunction-associated fatty liver disease in U.S. adults: evidence from NHANES 2017–2018 - Ming-yu He; Xin-jie Du; Yi-ming Liu (BMC Gastroenterology)
Abstract Background Metabolic dysfunction-associated fatty liver disease (MAFLD) is increasingly prevalent, and systemic inflammation markers may play a role in its pathogenesis. This study aimed to investigate the relationship between neutrophil-albumin ratio (NAR) and MAFLD. Methods This population-based study was performed using data from NHANES 2017–2018 and included 4526 individuals with a median age of 44 years old, and the males account for 46.13% (n = 2088). Ultrasound-defined MAFLD was diagnosed using a controlled attenuation parameter (CAP) threshold of ≥ 285 dB/m. Differences in baseline characteristics between patients with CAP ≥ 285 dB/m and < 285 dB/m were analyzed. A generalized additive model (GAM) and restricted cubic splines (RCS) were applied to explore the nonlinear relationship between NAR and CAP, followed by generalized linear models (GLMs). Threshold effect analysis was performed to identify the inflection point in the nonlinear relationship. CAP-related variables were ranked using XG Boost and random forest algorithms, and predictive models were developed and evaluated. Results The study population included 1,503 patients with CAP ≥ 285 dB/m. NAR was significantly elevated in subjects with CAP ≥ 285 dB/m (P < 0.001), and nonlinear relationships between NAR and CAP were observed. NAR was positively associated with CAP in three GLMs, and this relationship remained after adjusting for confounding factors or dividing NAR into tertiles. Additionally, when NAR < 1.436, a one-unit rise in NAR was linked to a 3.304-fold increase in the risk of NAFLD (OR = 3.304, 95% CI: 2.649–4.122). The NAR-based random forest model showed the best predictive performance with AUC values of 0.978 (training) and 0.813 (validation). Conclusion NAR is positively associated with CAP, and the NAR-based random forest model is optimal for predicting MAFLD risk, highlighting the importance of NAR in predicting MAFLD.